R: Long-Run Variance of the Mean

lrvar {sandwich} | R Documentation

## Long-Run Variance of the Mean

### Description

Convenience function for computing the long-run variance (matrix) of a (possibly multivariate) series of observations.

### Usage

    lrvar(x, type = c("Andrews", "Newey-West"), prewhite = TRUE, adjust = TRUE, ...)

### Arguments

`x` | numeric vector, matrix, or time series.
`type` | character specifying the type of estimator, i.e., whether `kernHAC` for the Andrews quadratic spectral kernel HAC estimator is used or `NeweyWest` for the Newey-West Bartlett HAC estimator.
`prewhite` | logical or integer. Should the series be prewhitened? Passed to `kernHAC` or `NeweyWest`.
`adjust` | logical. Should a finite sample adjustment be made? Passed to `kernHAC` or `NeweyWest`.
`...` | further arguments passed on to `kernHAC` or `NeweyWest`.

### Details

`lrvar` is a simple wrapper function for computing the long-run variance (matrix) of a (possibly multivariate) series `x`. First, this simply fits a linear regression model `x ~ 1` by `lm`. Second, the corresponding variance of the mean(s) is estimated either by `kernHAC` (Andrews quadratic spectral kernel HAC estimator) or by `NeweyWest` (Newey-West Bartlett HAC estimator).

### Value

For a univariate series `x` a scalar variance is computed. For a multivariate series `x` the covariance matrix is computed.

### See Also

`kernHAC`, `NeweyWest`, `vcovHAC`

### Examples

    set.seed(1)
    ## iid series (with variance of mean 1/n)
    ## and Andrews kernel HAC (with prewhitening)
    x <- rnorm(100)
    lrvar(x)

    ## analogous multivariate case with Newey-West estimator (without prewhitening)
    y <- matrix(rnorm(200), ncol = 2)
    lrvar(y, type = "Newey-West", prewhite = FALSE)

    ## AR(1) series with autocorrelation 0.9
    z <- filter(rnorm(100), 0.9, method = "recursive")
    lrvar(z)

[Package sandwich version 2.3-4 Index]